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Accelerating Stochastic Sequential Quadratic Programming for Equality Constrained Optimization using Predictive Variance Reduction

arXiv 2204.04161 ↗First posted 8 Apr 2022math.OCcited by 13

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Abstract (arXiv)

In this paper, we propose a stochastic method for solving equality constrained optimization problems that utilizes predictive variance reduction. Specifically, we develop a method based on the sequential quadratic programming paradigm that employs variance reduction in the gradient approximations. Under reasonable assumptions, we prove that a measure of first-order stationarity evaluated at the iterates generated by our proposed algorithm converges to zero in expectation from arbitrary starting points, for both constant and adaptive step size strategies. Finally, we demonstrate the practical performance of our proposed algorithm on constrained binary classification problems that arise in machine learning.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 8 Apr 2022.

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Citing papers per year: 2022: 1; 2023: 3; 2024: 5; 2025: 1; 2026: 3.

13 papers.

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  1. 2022Fully Stochastic Trust-Region Sequential Quadratic Programming for Equality-Constrained Optimization Problemscited by 21
  2. 2024Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guaranteescited by 11
  3. 2023A Sequential Quadratic Programming Method with High Probability Complexity Bounds for Nonlinear Equality Constrained Stochastic Optimizationcited by 8
  4. 2023A Stochastic-Gradient-based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problemscited by 8
  5. 2023Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimizationcited by 5
  6. 2024Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Modelscited by 5
  7. 2025High Probability Complexity Bounds of Trust-Region Stochastic Sequential Quadratic Programming with Heavy-Tailed Noisecited by 4
  8. 2024Second-order Information Promotes Mini-Batch Robustness in Variance-Reduced Gradientscited by 3
  9. 2024Single-Loop Deterministic and Stochastic Interior-Point Algorithms for Nonlinearly Constrained Optimizationcited by 1
  10. 2024Stochastic interior-point methods for smooth conic optimization with applicationscited by 1
  11. 2026A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Methodcited by 1
  12. 2026TRSVR: An Adaptive Stochastic Trust-Region Method with Variance Reductioncited by 0
1 more
  1. 2026TR-SSQP: A Trust-Region Method for Constrained Stochastic Optimization under Heavy-Tailed Noisecited by 0